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    <p>To use OpenCV machine learning algorithms, you have to write some wrapper classes:</p> <p><strong>1. First parent class</strong></p> <pre><code>class StatModel(object): '''parent class - starting point to add abstraction''' def load(self, fn): self.model.load(fn) def save(self, fn): self.model.save(fn) </code></pre> <p><strong>2. Finally SvM wrapper:</strong></p> <pre><code>class SVM(StatModel): '''wrapper for OpenCV SimpleVectorMachine algorithm''' def __init__(self): self.model = cv2.SVM() def train(self, samples, responses): #setting algorithm parameters params = dict( kernel_type = cv2.SVM_LINEAR, svm_type = cv2.SVM_C_SVC, C = 1 ) self.model.train(samples, responses, params = params) def predict(self, samples): return np.float32( [self.model.predict(s) for s in samples]) </code></pre> <p><strong>3.Example usage:</strong></p> <pre><code>import numpy as np import cv2 samples = np.array(np.random.random((4,2)), dtype = np.float32) y_train = np.array([1.,0.,0.,1.], dtype = np.float32) clf = SVM() clf.train(samples, y_train) y_val = clf.predict(samples) </code></pre> <p><strong>Setting parameters</strong></p> <p>Setting parameters is simple - just write a dictionary that holds the parameters as keys. You should look original documentation to see all possible parameters and allowed values: <a href="http://opencv.itseez.com/modules/ml/doc/support_vector_machines.html#cvsvmparams">http://opencv.itseez.com/modules/ml/doc/support_vector_machines.html#cvsvmparams</a></p> <p>Yes, possible values for svm_type and kernel_type are in C++, but there is easy way to convert those constants into Python representation, for example CvSVM::C_SVC is written as cv2.SVM_C_SVC in Python.</p> <p><strong>Prelude</strong> To get more wrappers for machine learning algorithms, look into <em>letter-recog.py</em> example in your opencv examples on disk or open url of OpenCV repository: <a href="https://github.com/Itseez/opencv/tree/master/samples/python2">https://github.com/Itseez/opencv/tree/master/samples/python2</a></p>
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    1. COThis looks very promising. I have registered at ros.org but when I enter my name and password for the link to python samples, I get this dialog box and cannot get past it. "To view this page, you must log in to this area on code.ros.org:443:" Is there a special name/password for this svn area, beyond my own?
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    2. COHi! Sadly they cancelled public view for this repository. I'll post some workaround very soon.
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